Take home data science problem from a ride share company.

This project is part of Springboard's Data science career track program and includes a data science take home challenge which has been adapted from an actual Ultimate Technologies (name changed) data science challenge.

It covers three parts:
Part 1 ‑ Exploratory data analysis
Part 2 ‑ Experiment and metrics design
Part 3 ‑ Predictive modeling

The questions for the project can be found in data/ultimate_data_science_challenge.pdf file.

Data description
● city: city this user signed up in
● phone: primary device for this user
● signup_date: date of account registration; in the form ‘YYYY MM DD’
● last_trip_date: the last time this user completed a trip; in the form ‘YYYY MM DD’
● avg_dist: the average distance in miles per trip taken in the first 30 days after signup
● avg_rating_by_driver: the rider’s average rating over all of their trips
● avg_rating_of_driver: the rider’s average rating of their drivers over all of their trips
● surge_pct: the percent of trips taken with surge multiplier > 1
● avg_surge: The average surge multiplier over all of this user’s trips
● trips_in_first_30_days: the number of trips this user took in the first 30 days after
signing up
● ultimate_black_user: TRUE if the user took an Ultimate Black in their first 30 days;
FALSE otherwise
● weekday_pct: the percent of the user’s trips occurring during a weekday